Continuous functions minimization by dynamic random search technique
Continuous functions minimization by dynamic random search technique
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DOI：
10.1016/j.apm.2006.08.015
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发表时间：
2007-10
影响因子：
5
通讯作者：
C. Hamzaçebi;Fevzi Kutay
中科院分区：
文献类型：
--
作者：
C. Hamzaçebi;Fevzi Kutay
随机搜索技术是启发式算法中最简单的一种。文献中指出，使用基本的随机搜索技术，找到全局最小值的概率等于1，但要达到全局最小值需要花费太多的时间。改进基本的随机搜索技术可以减少求解时间。为了快速得到全局最小值，提出了一种新的随机搜索算法。这种算法被称为动态随机搜索技术(DRASET)。DRASET算法分为两个阶段，即一般搜索阶段和基于一般解的局部搜索阶段。保留了一般搜索过程中找到的最优解的相关知识，然后将该知识作为局部搜索的初始值。用15个测试问题对DRASET的性能进行了测试，得到了满意的结果。
Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to 1 by using the basic random search technique, but it takes too much time to reach the global minimum. Improving the basic random search technique may decrease the solution time. In this study, in order to obtain the global minimum fastly, a new random search algorithm is suggested. This algorithm is called as the Dynamic Random Search Technique (DRASET). DRASET consists of two phases, which are general search and local search based on general solution. Knowledge related to the best solution found in the process of general search is kept and then that knowledge is used as initial value of local search. DRASET’s performance was experimented with 15 test problems and satisfactory results were obtained.